Relevant bibliographies by topics / Cross-view Learning

Academic literature on the topic 'Cross-view Learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Cross-view Learning"

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Nie, Weizhi, Anan Liu, Wenhui Li, and Yuting Su. "Cross-view action recognition by cross-domain learning." Image and Vision Computing 55 (November 2016): 109–18. http://dx.doi.org/10.1016/j.imavis.2016.04.011.

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Wang, Xiuhui, and Wei Qi Yan. "Cross-view gait recognition through ensemble learning." Neural Computing and Applications 32, no. 11 (May 16, 2019): 7275–87. http://dx.doi.org/10.1007/s00521-019-04256-z.

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Zhang, Chengkun, Huicheng Zheng, and Jianhuang Lai. "Cross-View Action Recognition Based on Hierarchical View-Shared Dictionary Learning." IEEE Access 6 (2018): 16855–68. http://dx.doi.org/10.1109/access.2018.2815611.

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Zhang, Yuqi, Yongzhen Huang, Shiqi Yu, and Liang Wang. "Cross-View Gait Recognition by Discriminative Feature Learning." IEEE Transactions on Image Processing 29 (2020): 1001–15. http://dx.doi.org/10.1109/tip.2019.2926208.

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Zheng, Jingjing, Zhuolin Jiang, and Rama Chellappa. "Cross-View Action Recognition via Transferable Dictionary Learning." IEEE Transactions on Image Processing 25, no. 6 (June 2016): 2542–56. http://dx.doi.org/10.1109/tip.2016.2548242.

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ZALL, R., and M. R. KEYVANPOUR. "Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation." Advances in Electrical and Computer Engineering 16, no. 2 (2016): 111–24. http://dx.doi.org/10.4316/aece.2016.02015.

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Ding, Zhengming, and Yun Fu. "Dual Low-Rank Decompositions for Robust Cross-View Learning." IEEE Transactions on Image Processing 28, no. 1 (January 2019): 194–204. http://dx.doi.org/10.1109/tip.2018.2865885.

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Borgia, Alessandro, Yang Hua, Elyor Kodirov, and Neil M. Robertson. "Cross-View Discriminative Feature Learning for Person Re-Identification." IEEE Transactions on Image Processing 27, no. 11 (November 2018): 5338–49. http://dx.doi.org/10.1109/tip.2018.2851098.

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Dai, Ju, Ying Zhang, Huchuan Lu, and Hongyu Wang. "Cross-view semantic projection learning for person re-identification." Pattern Recognition 75 (March 2018): 63–76. http://dx.doi.org/10.1016/j.patcog.2017.04.022.

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CHEN, Xue, Chunheng WANG, Baihua XIAO, and Song GAO. "Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition." IEICE Transactions on Information and Systems E97.D, no. 12 (2014): 3239–43. http://dx.doi.org/10.1587/transinf.2014edl8095.

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Dissertations / Theses on the topic "Cross-view Learning"

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Zhang, Li. "Cross-view learning." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/43185.

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Key to achieving more efficient machine intelligence is the capability to analysing and understanding data across different views - which can be camera views or modality views (such as visual and textual). One generic learning paradigm for automated understanding data from different views called cross-view learning which includes cross-view matching, cross-view fusion and cross-view generation. Specifically, this thesis investigates two of them, cross-view matching and cross-view generation, by developing new methods for addressing the following specific computer vision problems. The first problem is cross-view matching for person re-identification which a person is captured by multiple non-overlapping camera views, the objective is to match him/her across views among a large number of imposters. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training samples. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power for cross-view matching. To that end, this thesis proposes to overcome the SSS problem in subspace learning by matching cross-view data in a discriminative null space of the training data. The second problem is cross-view matching for zero-shot learning where data are drawn from different modalities each for a different view (e.g. visual or textual), versus single-modal data considered in the first problem. This is inherently more challenging as the gap between different views becomes larger. Specifically, the zero-shot learning problem can be solved if the visual representation/view of the data (object) and its textual view are matched. Moreover, it requires learning a joint embedding space where different view data can be projected to for nearest neighbour search. This thesis argues that the key to make zero-shot learning models succeed is to choose the right embedding space. Different from most existing zero-shot learning models utilising a textual or an intermediate space as the embedding space for achieving crossview matching, the proposed method uniquely explores the visual space as the embedding space. This thesis finds that in the visual space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. Moreover, a natural mechanism for multiple textual modalities optimised jointly in an end-to-end manner in this model demonstrates significant advantages over existing methods. The last problem is cross-view generation for image captioning which aims to automatically generate textual sentences from visual images. Most existing image captioning studies are limited to investigate variants of deep learning-based image encoders, improving the inputs for the subsequent deep sentence decoders. Existing methods have two limitations: (i) They are trained to maximise the likelihood of each ground-truth word given the previous ground-truth words and the image, termed Teacher-Forcing. This strategy may cause a mismatch between training and testing since at test-time the model uses the previously generated words from the model distribution to predict the next word. This exposure bias can result in error accumulation in sentence generation during test time, since the model has never been exposed to its own predictions. (ii) The training supervision metric, such as the widely used cross entropy loss, is different from the evaluation metrics at test time. In other words, the model is not directly optimised towards the task expectation. This learned model is therefore suboptimal. One main underlying reason responsible is that the evaluation metrics are non-differentiable and therefore much harder to be optimised against. This thesis overcomes the problems as above by exploring the reinforcement learning idea. Specifically, a novel actor-critic based learning approach is formulated to directly maximise the reward - the actual Natural Language Processing quality metrics of interest. As compared to existing reinforcement learning based captioning models, the new method has the unique advantage of a per-token advantage and value computation is enabled leading to better model training.
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Gupta, Parth Alokkumar. "Cross-view Embeddings for Information Retrieval." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/78457.

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In this dissertation, we deal with the cross-view tasks related to information retrieval using embedding methods. We study existing methodologies and propose new methods to overcome their limitations. We formally introduce the concept of mixed-script IR, which deals with the challenges faced by an IR system when a language is written in different scripts because of various technological and sociological factors. Mixed-script terms are represented by a small and finite feature space comprised of character n-grams. We propose the cross-view autoencoder (CAE) to model such terms in an abstract space and CAE provides the state-of-the-art performance. We study a wide variety of models for cross-language information retrieval (CLIR) and propose a model based on compositional neural networks (XCNN) which overcomes the limitations of the existing methods and achieves the best results for many CLIR tasks such as ad-hoc retrieval, parallel sentence retrieval and cross-language plagiarism detection. We empirically test the proposed models for these tasks on publicly available datasets and present the results with analyses. In this dissertation, we also explore an effective method to incorporate contextual similarity for lexical selection in machine translation. Concretely, we investigate a feature based on context available in source sentence calculated using deep autoencoders. The proposed feature exhibits statistically significant improvements over the strong baselines for English-to-Spanish and English-to-Hindi translation tasks. Finally, we explore the the methods to evaluate the quality of autoencoder generated representations of text data and analyse its architectural properties. For this, we propose two metrics based on reconstruction capabilities of the autoencoders: structure preservation index (SPI) and similarity accumulation index (SAI). We also introduce a concept of critical bottleneck dimensionality (CBD) below which the structural information is lost and present analyses linking CBD and language perplexity.
En esta disertación estudiamos problemas de vistas-múltiples relacionados con la recuperación de información utilizando técnicas de representación en espacios de baja dimensionalidad. Estudiamos las técnicas existentes y proponemos nuevas técnicas para solventar algunas de las limitaciones existentes. Presentamos formalmente el concepto de recuperación de información con escritura mixta, el cual trata las dificultades de los sistemas de recuperación de información cuando los textos contienen escrituras en distintos alfabetos debido a razones tecnológicas y socioculturales. Las palabras en escritura mixta son representadas en un espacio de características finito y reducido, compuesto por n-gramas de caracteres. Proponemos los auto-codificadores de vistas-múltiples (CAE, por sus siglas en inglés) para modelar dichas palabras en un espacio abstracto, y esta técnica produce resultados de vanguardia. En este sentido, estudiamos varios modelos para la recuperación de información entre lenguas diferentes (CLIR, por sus siglas en inglés) y proponemos un modelo basado en redes neuronales composicionales (XCNN, por sus siglas en inglés), el cual supera las limitaciones de los métodos existentes. El método de XCNN propuesto produce mejores resultados en diferentes tareas de CLIR tales como la recuperación de información ad-hoc, la identificación de oraciones equivalentes en lenguas distintas y la detección de plagio entre lenguas diferentes. Para tal efecto, realizamos pruebas experimentales para dichas tareas sobre conjuntos de datos disponibles públicamente, presentando los resultados y análisis correspondientes. En esta disertación, también exploramos un método eficiente para utilizar similitud semántica de contextos en el proceso de selección léxica en traducción automática. Específicamente, proponemos características extraídas de los contextos disponibles en las oraciones fuentes mediante el uso de auto-codificadores. El uso de las características propuestas demuestra mejoras estadísticamente significativas sobre sistemas de traducción robustos para las tareas de traducción entre inglés y español, e inglés e hindú. Finalmente, exploramos métodos para evaluar la calidad de las representaciones de datos de texto generadas por los auto-codificadores, a la vez que analizamos las propiedades de sus arquitecturas. Como resultado, proponemos dos nuevas métricas para cuantificar la calidad de las reconstrucciones generadas por los auto-codificadores: el índice de preservación de estructura (SPI, por sus siglas en inglés) y el índice de acumulación de similitud (SAI, por sus siglas en inglés). También presentamos el concepto de dimensión crítica de cuello de botella (CBD, por sus siglas en inglés), por debajo de la cual la información estructural se deteriora. Mostramos que, interesantemente, la CBD está relacionada con la perplejidad de la lengua.
En aquesta dissertació estudiem els problemes de vistes-múltiples relacionats amb la recuperació d'informació utilitzant tècniques de representació en espais de baixa dimensionalitat. Estudiem les tècniques existents i en proposem unes de noves per solucionar algunes de les limitacions existents. Presentem formalment el concepte de recuperació d'informació amb escriptura mixta, el qual tracta les dificultats dels sistemes de recuperació d'informació quan els textos contenen escriptures en diferents alfabets per motius tecnològics i socioculturals. Les paraules en escriptura mixta són representades en un espai de característiques finit i reduït, composat per n-grames de caràcters. Proposem els auto-codificadors de vistes-múltiples (CAE, per les seves sigles en anglès) per modelar aquestes paraules en un espai abstracte, i aquesta tècnica produeix resultats d'avantguarda. En aquest sentit, estudiem diversos models per a la recuperació d'informació entre llengües diferents (CLIR , per les sevas sigles en anglès) i proposem un model basat en xarxes neuronals composicionals (XCNN, per les sevas sigles en anglès), el qual supera les limitacions dels mètodes existents. El mètode de XCNN proposat produeix millors resultats en diferents tasques de CLIR com ara la recuperació d'informació ad-hoc, la identificació d'oracions equivalents en llengües diferents, i la detecció de plagi entre llengües diferents. Per a tal efecte, realitzem proves experimentals per aquestes tasques sobre conjunts de dades disponibles públicament, presentant els resultats i anàlisis corresponents. En aquesta dissertació, també explorem un mètode eficient per utilitzar similitud semàntica de contextos en el procés de selecció lèxica en traducció automàtica. Específicament, proposem característiques extretes dels contextos disponibles a les oracions fonts mitjançant l'ús d'auto-codificadors. L'ús de les característiques proposades demostra millores estadísticament significatives sobre sistemes de traducció robustos per a les tasques de traducció entre anglès i espanyol, i anglès i hindú. Finalment, explorem mètodes per avaluar la qualitat de les representacions de dades de text generades pels auto-codificadors, alhora que analitzem les propietats de les seves arquitectures. Com a resultat, proposem dues noves mètriques per quantificar la qualitat de les reconstruccions generades pels auto-codificadors: l'índex de preservació d'estructura (SCI, per les seves sigles en anglès) i l'índex d'acumulació de similitud (SAI, per les seves sigles en anglès). També presentem el concepte de dimensió crítica de coll d'ampolla (CBD, per les seves sigles en anglès), per sota de la qual la informació estructural es deteriora. Mostrem que, de manera interessant, la CBD està relacionada amb la perplexitat de la llengua.
Gupta, PA. (2017). Cross-view Embeddings for Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78457
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Rohlén, Andreas. "UAV geolocalization in Swedish fields and forests using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300390.

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The ability for unmanned autonomous aerial vehicles (UAV) to localize themselves in an environment is fundamental for them to be able to function, even if they do not have access to a global positioning system. Recently, with the success of deep learning in vision based tasks, there have been some proposed methods for absolute geolocalization using vison based deep learning with satellite and UAV images. Most of these are only tested in urban environments, which begs the question: How well do they work in non-urban areas like forests and fields? One drawback of deep learning is that models are often regarded as black boxes, as it is hard to know why the models make the predictions they do, i.e. what information is important and is used for the prediction. To solve this, several neural network interpretation methods have been developed. These methods provide explanations so that we may understand these models better. This thesis investigates the localization accuracy of one geolocalization method in both urban and non-urban environments as well as applies neural network interpretation in order to see if it can explain the potential difference in localization accuracy of the method in these different environments. The results show that the method performs best in urban environments, getting a mean absolute horizontal error of 38.30m and a mean absolute vertical error of 16.77m, while it performed significantly worse in non-urban environments, getting a mean absolute horizontal error of 68.11m and a mean absolute vertical error 22.83m. Further, the results show that if the satellite images and images from the unmanned aerial vehicle are collected during different seasons of the year, the localization accuracy is even worse, resulting in a mean absolute horizontal error of 86.91m and a mean absolute vertical error of 23.05m. The neural network interpretation did not aid in providing an explanation for why the method performs worse in non-urban environments and is not suitable for this kind of problem.
Obemannade autonoma luftburna fordons (UAV) förmåga att lokaliera sig själva är fundamental för att de ska fungera, även om de inte har tillgång till globala positioneringssystem. Med den nyliga framgången hos djupinlärning applicerat på visuella problem har det kommit metoder för absolut geolokalisering med visuell djupinlärning med satellit- och UAV-bilder. De flesta av dessa metoder har bara blivit testade i stadsmiljöer, vilket leder till frågan: Hur väl fungerar dessa metoder i icke-urbana områden som fält och skogar? En av nackdelarna med djupinlärning är att dessa modeller ofta ses som svarta lådor eftersom det är svårt att veta varför modellerna gör de gissningar de gör, alltså vilken information som är viktig och används för gissningen. För att lösa detta har flera metoder för att tolka neurala nätverk utvecklats. Dessa metoder ger förklaringar så att vi kan förstå dessa modeller bättre. Denna uppsats undersöker lokaliseringsprecisionen hos en geolokaliseringsmetod i både urbana och icke-urbana miljöer och applicerar även en tolkningsmetod för neurala nätverk för att se ifall den kan förklara den potentialla skillnaden i precision hos metoden i dessa olika miljöer. Resultaten visar att metoden fungerar bäst i urbana miljöer där den får ett genomsnittligt absolut horisontellt lokaliseringsfel på 38.30m och ett genomsnittligt absolut vertikalt fel på 16.77m medan den presterade signifikant sämre i icke-urbana miljöer där den fick ett genomsnittligt absolut horisontellt lokaliseringsfel på 68.11m och ett genomsnittligt absolut vertikalt fel på 22.83m. Vidare visar resultaten att om satellitbilderna och UAV-bilderna är tagna från olika årstider blir lokaliseringsprecisionen ännu sämre, där metoden får genomsnittligt absolut horisontellt lokaliseringsfel på 86.91m och ett genomsnittligt absolut vertikalt fel på 23.05m. Tolkningsmetoden hjälpte inte i att förklara varför metoden fungerar sämre i icke-urbana miljöer och är inte passande att använda för denna sortens problem.
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Harahap, Faisal R. "Essays on Emerging Multinational Enterprises' Acquisitions in Developed Economies." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3540.

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This dissertation investigates emerging multinational enterprises (EMNEs)’s acquisitions of firms in developed economies (DE) through three distinctive but interrelated essays. Despite costs EMNEs must offset from the obvious cultural distance (CD) they encounter with limited exploitable advantages, EMNEs have continued to aggressively acquire firms in DE, suggesting there are ways for the EMNEs to effectively overcome CD. In Essay 1, using insights from the symbolic interaction paradigm in sociology, I developed the Dynamic Socio-Cultural Model (DSCM), to uncover the general process of cultural creation and change. At the core of the DSCM is the process of collective learning and adaptive interaction in every social system. Viewing EMNEs’ acquisitions in DE as a cultural event that leads to new shared cultural resources, DSCM shows culture is not as rigid as was typically conceptualized in the cross-cultural management literature. While the negative effect of CD may initially impede EMNEs, CD may be positively moderated by certain conditions of the involved cultures. In Essay 2, I extended DSCM and combined it with insights from the organizational learning literature to focus on EMNE’s choices of control mode and their performance implications. Performing event study and endogenous switching regression on 1157 EMNE’s acquisitions in 21 advanced economies, I found EMNEs have, on average, a positive post-acquisition performance. I also found being an EMNE from an emerging economy that underwent rapid industrialization and targeting a high-tech firm increases the probability for choosing a low-control mode. Moreover, EMNE acquirers choose control mode by strategically considering their unique characteristics to optimize performance. In Essay 3, using the same theoretical approach, I examined the target firms’ sources of value creation. Applying an event study on 167 acquisitions in North America made by EMNEs from 11 countries, I found EMNEs’ partial acquisitions in DE generate, on average, a positive target’s cumulative abnormal returns (CAR). There is also empirical support for several determinants of target’s value creation and moderation effects. In particular, I found target’s international experience attenuates the negative effect of CD on target CAR, while acquirer’s state-owned status exacerbates it. Overall, the three essays collectively contribute to research streams in EMNEs, seller’s view of M&A, and cultural change.
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Weatherholtz, Kodi. "Perceptual learning of systemic cross-category vowel variation." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429782580.

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Turner, Marianne. "Adult South Sudanese students in Australia : a systemic approach to the investigation of participation in cross-cultural learning /." Murdoch University Digital Theses Program, 2008. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20090902.11321.

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Awad, Ghada M. "MOTIVATION, PERSISTENCE, AND CROSS-CULTURAL AWARENESS: A STUDY OF COLLEGE STUDENTS LEARNING FOREIGN LANGUAGES." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1542036826465842.

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Jewett, Andrea L. "Effects of cross-age reciprocal peer tutoring on math fact acquisition with learning disabled students." The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1399890459.

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Kajasiche, Diana Tadala. "The impact of unplanned online learning due to Covid-19 on cross-cultural experiences and expectations on international African graduate students in the US." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu162687000181805.

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Ainapure, Abhijeet Narhar. "Application and Performance Enhancement of Intelligent Cross-Domain Fault Diagnosis in Rotating Machinery." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164772153736.

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Books on the topic "Cross-view Learning"

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Pennington, Martha C. English on the world stage: A cross-language view of code correspondencies and their effect on language contact and language learning. Hong Kong: City Polytechnic of Hong Kong, 1994.

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Schmidt-Lauff, Sabine. Adult education and lifelong learning: A European view as perceived by participants in an exchange programme. Hamburg: Kovac, 2003.

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Bier, Ada. La motivazione nell’insegnamento in CLIL. Venice: Edizioni Ca' Foscari, 2018. http://dx.doi.org/10.30687/978-88-6969-213-0.

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There are several studies in the literature that emphasize the link between CLIL and student motivation for learning. The same does not apply for teachers – who teach a non-language subject through a foreign language – whose motivation for teaching in CLIL should not be taken for granted. Our research is an inquiry in the Italian upper secondary school with a dual focus: a main focus on CLIL teachers and a secondary one on CLIL students. The main aim of this cross-sectional study is to offer a snapshot of the existing situation from the point of view of teachers’ and students’ perceptions one year after the introduction of the legal requirement for compulsory CLIL, with a view to reflecting on the present in order to hypothesize possible future developments. The obtained results – which confirm the association between the motivational dimension of the CLIL teacher with the cognitive, affective and relational ones, and with the motivational dimension of CLIL students – are interpreted and discussed in the light of the most recent theoretical developments and suggestions for future practice and research are offered.
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Bryson, Jane. Disciplinary Perspectives on Skill. Edited by John Buchanan, David Finegold, Ken Mayhew, and Chris Warhurst. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199655366.013.1.

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Skill is a ubiquitous term but it is not always commonly understood. This chapter demonstrates that our understanding of skill varies, often as a reflection of our disciplinary interests. Three cross disciplinary lenses are used to examine varying views of skill: its meaning, acquisition, utilisation, recognition, and impact. These lenses: political economy of skill; skill as an organisational resource; and learning theory, enable an exploration of economic, political science, sociology, industrial relations, human resource management, organisation studies, education and psychology perspectives. It is argued that rather than a single or even cross-disciplinary view, multiple perspectives on skill are essential to effective policy development and positive influence on individual and social wellbeing.
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Millie, Andrew, ed. Criminology and Public Theology. Policy Press, 2020. http://dx.doi.org/10.1332/policypress/9781529207392.001.0001.

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At a time when criminal justice systems appear to be in a permanent state of crisis, in this collection leading scholars from criminology and theology have come together to challenge criminal justice orthodoxy by questioning the dominance of retributive punishment. The book is a timely and unique contribution that considers alternatives which draw on Christian ideas of hope, mercy and restoration, rather than the norms of punishment, pain and retribution. Contributions focus on punishment, although wider criminal justice concerns in policing and probation, as well as mental health and surveillance are also considered. Promoting cross-disciplinary learning, the book is of interest to academics and students of criminology, public theology, socio-legal studies, legal philosophy and religious studies, as well as practitioners and policy makers. If the ideas discussed in this book are taken seriously they offer a fundamental challenge to the contemporary view of criminal justice.
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Steinberg, Claudia, and Benjamin Bonn, eds. Digitalisierung und Sportwissenschaft. Academia – ein Verlag in der Nomos Verlagsgesellschaft, 2021. http://dx.doi.org/10.5771/9783985720033.

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For a long time now, digitalization has arrived in movement, play, sport, and dance. In many areas, the analog can hardly be separated from the digital. The situation seems to be different in the education sector. Where does sports science stand? What approaches does this cross-sectional discipline offer? The contributions in this volume provide insights into the sports science debate on this topic. They outline overarching lines of discussion, present research results, and draw perspectives for the sports science debate with a view to (educational) political dimensions, the staging of teaching-learning settings, international discourses on new practices in the health sector, and the development of innovative research methods. With contributions by Prof. Dr. Claudia Steinberg, Dr. Benjamin Bonn, Lucas Abel, Peter Bickmann, Dr. Birgit Braumüller, Prof. em. Dr. David R. Buchanan, Christian Büning, Univ.-Prof. Dr. Ingo Froböse, Mai Geisen, Marco Grawunder, Dr. Christiopher Grieben, Stephani Howahl, Prof. Dr. Petra Jansen, Derya Kaptan, Simone Kieltyka, Jun.-Prof. Dr. Stefanie Klatt, Asst.-Prof. Maria Kosma, Univ.-Prof. Dr. Dr. Markus Raab, Ass.Prof. Dr. Daniel Rode, Dr. Helena Rudi, Dr. Kevin Rudolf, Univ.-Prof. Dr. Andrea Schaller, Gerrit Stassen, Dr. Ilka Staub, Chuck Tholl, Dr. Konstantin Wechsler, Jun.-Prof. Dr. Tobias Vogt, Univ.-Prof. Dr. Thomas Wendeborn, Constantin Wirth and Maren Zühlke.
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Holmes, Robyn M. Cultural Psychology. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780199343805.001.0001.

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Cultural psychology draws upon major psychological topics, theories, and principles to illustrate the importance of culture in psychological inquiry. It explores how culture broadly connects to psychological processing across diverse cultural communities and settings, highlighting its application to everyday life events and situations, and presenting culture as a complex medium in which individuals acquire skills, values, and abilities. One central theme is the view of culture as a mental and physical construct that individuals live, experience, share, perform, and learn; a second core theme is how culture shapes growth and development. Culture-specific and cross-cultural examples reveal connections between culture and psychological phenomena. The text is multidisciplinary and presents different perspectives on how culture shapes human phenomena. It provides an introduction to this field; covers the history of cultural psychology, cultural evolution, and cultural ecology; explains methods; and examines language and nonverbal communication, and cognition and perception. Topics investigating social behavior include the self, identity, and personality; social relationships, social attitudes, and intergroup contact in a global world; and social influence, aggression, violence, and war. Topics addressing growth and development include human development and its processes, transitions, and rituals across the life span; and socializing agents, socialization practices, and child activities. Additional topics explore emotion and motivation, mental health and psychopathology, and future directions for cultural psychology. Chapters contain teaching and learning tools, including case studies, multidisciplinary contributions, thought-provoking questions, class and experiential activities, a chapter summary, and additional print and media resources.
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Book chapters on the topic "Cross-view Learning"

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Bharti, Jyoti, and Lalit Lohiya. "Cross-View Gait Recognition Using Deep Learning Approach." In IOT with Smart Systems, 45–54. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3945-6_6.

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Yao, Zhenjun, Zhaoxiang Zhang, Maodi Hu, and Yunhong Wang. "Cross-View Gait-Based Gender Classification by Transfer Learning." In Lecture Notes in Computer Science, 79–87. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03731-8_8.

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Capponi, Cécile, and Sokol Koço. "Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods." In Unsupervised and Semi-Supervised Learning, 161–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01872-6_7.

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Zhang, Xiaohui, Huifang Ma, Fanyi Yang, Zhixin Li, and Liang Chang. "Cross-View Contrastive Learning for Knowledge-Aware Session-Based Recommendation." In Lecture Notes in Computer Science, 60–73. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20868-3_5.

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Cao, Rui, Jiasong Zhu, Qing Li, Qian Zhang, Qingquan Li, Bozhi Liu, and Guoping Qiu. "Learning Spatial-Aware Cross-View Embeddings for Ground-to-Aerial Geolocalization." In Lecture Notes in Computer Science, 57–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34120-6_5.

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Zhou, Qing, Ronggang Zhu, Yuelei Xu, and Zhaoxiang Zhang. "Cross-View Images Matching and Registration Technology Based on Deep Learning." In Lecture Notes in Computer Science, 725–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87355-4_60.

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Jones, Alison, and Kuni Jenkins. "Cross-cultural Engagement in Higher Education Classrooms: a Critical View of Dialogue." In Learning and Teaching Across Cultures in Higher Education, 133–52. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230590427_8.

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Li, Tong, Kai Xuan, Zhong Xue, Lei Chen, Lichi Zhang, and Dahong Qian. "Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning." In Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 96–105. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60548-3_10.

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Khurshid, Numan, Talha Hanif, Mohbat Tharani, and Murtaza Taj. "Cross-View Image Retrieval - Ground to Aerial Image Retrieval Through Deep Learning." In Neural Information Processing, 210–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36711-4_19.

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Zhang, Zhaoxiang, Jun Tang, Yuhang Zhao, Yunhong Wang, and Jianyun Liu. "Active Learning for Transferrable Object Classification in Cross-View Traffic Scene Surveillance." In Advances in Multimedia Information Processing – PCM 2012, 369–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34778-8_34.

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Conference papers on the topic "Cross-view Learning"

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Liu, Liu, and Hairong Qi. "Discriminative Cross-View Binary Representation Learning." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00193.

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Zheng, Liangfeng, Yongzhi Li, and Yadong Mu. "Learning Factorized Cross-View Fusion for Multi-View Crowd Counting." In 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2021. http://dx.doi.org/10.1109/icme51207.2021.9428284.

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Zheng, Jingjing, and Zhuolin Jiang. "Learning View-Invariant Sparse Representations for Cross-View Action Recognition." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.394.

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Wang, Jiang, Xiaohan Nie, Yin Xia, Ying Wu, and Song-Chun Zhu. "Cross-View Action Modeling, Learning, and Recognition." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.339.

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Li, Ang, Huiyi Hu, Piotr Mirowski, and Mehrdad Farajtabar. "Cross-View Policy Learning for Street Navigation." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00819.

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Lee, Chun-Chieh, Chi-Hung Chuang, Fanzi Wu, Luo-Wei Tsai, and Kuo-Chin Fan. "Cross view gait recognition by metric learning." In 2014 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2014. http://dx.doi.org/10.1109/icce-tw.2014.6904112.

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Tong, Suibing, Hefei Ling, Yuzhuo Fu, and Dan Wang. "Cross-View Gait Identification with Embedded Learning." In the. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3126686.3126753.

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Chen, Shin-Yu, Jun-Wei Hsieh, and Duan-Yu Chen. "Cross-view object identification using principal color transformation." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580787.

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Jing, Longlong, Ling Zhang, and Yingli Tian. "Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00174.

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Huang, Lang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, and Toshihiko Yamasaki. "Learning Where to Learn in Cross-View Self-Supervised Learning." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01405.

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